code: def pad(array, pad_width, mode, **kwargs):
      """
      Pads an array.
  
      Parameters
      ----------
      array : array_like of rank N
          Input array
      pad_width : {sequence, array_like, int}
          Number of values padded to the edges of each axis.
          ((before_1, after_1), ... (before_N, after_N)) unique pad widths
          for each axis.
          ((before, after),) yields same before and after pad for each axis.
          (pad,) or int is a shortcut for before = after = pad width for all
          axes.
      mode : str or function
          One of the following string values or a user supplied function.
  
          'constant'
              Pads with a constant value.
          'edge'
              Pads with the edge values of array.
          'linear_ramp'
              Pads with the linear ramp between end_value and the
              array edge value.
          'maximum'
              Pads with the maximum value of all or part of the
              vector along each axis.
          'mean'
              Pads with the mean value of all or part of the
              vector along each axis.
          'median'
              Pads with the median value of all or part of the
              vector along each axis.
          'minimum'
              Pads with the minimum value of all or part of the
              vector along each axis.
          'reflect'
              Pads with the reflection of the vector mirrored on
              the first and last values of the vector along each
              axis.
          'symmetric'
              Pads with the reflection of the vector mirrored
              along the edge of the array.
          'wrap'
              Pads with the wrap of the vector along the axis.
              The first values are used to pad the end and the
              end values are used to pad the beginning.
          <function>
              Padding function, see Notes.
      stat_length : sequence or int, optional
          Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
          values at edge of each axis used to calculate the statistic value.
  
          ((before_1, after_1), ... (before_N, after_N)) unique statistic
          lengths for each axis.
  
          ((before, after),) yields same before and after statistic lengths
          for each axis.
  
          (stat_length,) or int is a shortcut for before = after = statistic
          length for all axes.
  
          Default is ``None``, to use the entire axis.
      constant_values : sequence or int, optional
          Used in 'constant'.  The values to set the padded values for each
          axis.
  
          ((before_1, after_1), ... (before_N, after_N)) unique pad constants
          for each axis.
  
          ((before, after),) yields same before and after constants for each
          axis.
  
          (constant,) or int is a shortcut for before = after = constant for
          all axes.
  
          Default is 0.
      end_values : sequence or int, optional
          Used in 'linear_ramp'.  The values used for the ending value of the
          linear_ramp and that will form the edge of the padded array.
  
          ((before_1, after_1), ... (before_N, after_N)) unique end values
          for each axis.
  
          ((before, after),) yields same before and after end values for each
          axis.
  
          (constant,) or int is a shortcut for before = after = end value for
          all axes.
  
          Default is 0.
      reflect_type : {'even', 'odd'}, optional
          Used in 'reflect', and 'symmetric'.  The 'even' style is the
          default with an unaltered reflection around the edge value.  For
          the 'odd' style, the extended part of the array is created by
          subtracting the reflected values from two times the edge value.
  
      Returns
      -------
      pad : ndarray
          Padded array of rank equal to `array` with shape increased
          according to `pad_width`.
  
      Notes
      -----
      .. versionadded:: 1.7.0
  
      For an array with rank greater than 1, some of the padding of later
      axes is calculated from padding of previous axes.  This is easiest to
      think about with a rank 2 array where the corners of the padded array
      are calculated by using padded values from the first axis.
  
      The padding function, if used, should return a rank 1 array equal in
      length to the vector argument with padded values replaced. It has the
      following signature::
  
          padding_func(vector, iaxis_pad_width, iaxis, kwargs)
  
      where
  
          vector : ndarray
              A rank 1 array already padded with zeros.  Padded values are
              vector[:pad_tuple[0]] and vector[-pad_tuple[1]:].
          iaxis_pad_width : tuple
              A 2-tuple of ints, iaxis_pad_width[0] represents the number of
              values padded at the beginning of vector where
              iaxis_pad_width[1] represents the number of values padded at
              the end of vector.
          iaxis : int
              The axis currently being calculated.
          kwargs : dict
              Any keyword arguments the function requires.
  
      Examples
      --------
      >>> a = [1, 2, 3, 4, 5]
      >>> np.pad(a, (2,3), 'constant', constant_values=(4, 6))
      array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])
  
      >>> np.pad(a, (2, 3), 'edge')
      array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])
  
      >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
      array([ 5,  3,  1,  2,  3,  4,  5,  2, -1, -4])
  
      >>> np.pad(a, (2,), 'maximum')
      array([5, 5, 1, 2, 3, 4, 5, 5, 5])
  
      >>> np.pad(a, (2,), 'mean')
      array([3, 3, 1, 2, 3, 4, 5, 3, 3])
  
      >>> np.pad(a, (2,), 'median')
      array([3, 3, 1, 2, 3, 4, 5, 3, 3])
  
      >>> a = [[1, 2], [3, 4]]
      >>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
      array([[1, 1, 1, 2, 1, 1, 1],
             [1, 1, 1, 2, 1, 1, 1],
             [1, 1, 1, 2, 1, 1, 1],
             [1, 1, 1, 2, 1, 1, 1],
             [3, 3, 3, 4, 3, 3, 3],
             [1, 1, 1, 2, 1, 1, 1],
             [1, 1, 1, 2, 1, 1, 1]])
  
      >>> a = [1, 2, 3, 4, 5]
      >>> np.pad(a, (2, 3), 'reflect')
      array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
  
      >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
      array([-1,  0,  1,  2,  3,  4,  5,  6,  7,  8])
  
      >>> np.pad(a, (2, 3), 'symmetric')
      array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
  
      >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
      array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
  
      >>> np.pad(a, (2, 3), 'wrap')
      array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
  
      >>> def pad_with(vector, pad_width, iaxis, kwargs):
      ...     pad_value = kwargs.get('padder', 10)
      ...     vector[:pad_width[0]] = pad_value
      ...     vector[-pad_width[1]:] = pad_value
      ...     return vector
      >>> a = np.arange(6)
      >>> a = a.reshape((2, 3))
      >>> np.pad(a, 2, pad_with)
      array([[10, 10, 10, 10, 10, 10, 10],
             [10, 10, 10, 10, 10, 10, 10],
             [10, 10,  0,  1,  2, 10, 10],
             [10, 10,  3,  4,  5, 10, 10],
             [10, 10, 10, 10, 10, 10, 10],
             [10, 10, 10, 10, 10, 10, 10]])
      >>> np.pad(a, 2, pad_with, padder=100)
      array([[100, 100, 100, 100, 100, 100, 100],
             [100, 100, 100, 100, 100, 100, 100],
             [100, 100,   0,   1,   2, 100, 100],
             [100, 100,   3,   4,   5, 100, 100],
             [100, 100, 100, 100, 100, 100, 100],
             [100, 100, 100, 100, 100, 100, 100]])
      """
      if not np.asarray(pad_width).dtype.kind == "i":
          raise TypeError("`pad_width` must be of integral type.")
  
      narray = np.array(array)
      pad_width = _as_pairs(pad_width, narray.ndim, as_index=True)
  
      allowedkwargs = {
          "constant": ["constant_values"],
          "edge": [],
          "linear_ramp": ["end_values"],
          "maximum": ["stat_length"],
          "mean": ["stat_length"],
          "median": ["stat_length"],
          "minimum": ["stat_length"],
          "reflect": ["reflect_type"],
          "symmetric": ["reflect_type"],
          "wrap": [],
      }
  
      kwdefaults = {
          "stat_length": None,
          "constant_values": 0,
          "end_values": 0,
          "reflect_type": "even",
      }
  
      if isinstance(mode, np.compat.basestring):
          # Make sure have allowed kwargs appropriate for mode
          for key in kwargs:
              if key not in allowedkwargs[mode]:
                  raise ValueError(
                      "%s keyword not in allowed keywords %s"
                      % (key, allowedkwargs[mode])
                  )
  
          # Set kwarg defaults
          for kw in allowedkwargs[mode]:
              kwargs.setdefault(kw, kwdefaults[kw])
  
          # Need to only normalize particular keywords.
          for i in kwargs:
              if i == "stat_length":
                  kwargs[i] = _as_pairs(
                      kwargs[i], narray.ndim, as_index=True
                  )
              if i in ["end_values", "constant_values"]:
                  kwargs[i] = _as_pairs(kwargs[i], narray.ndim)
      else:
          # Drop back to old, slower np.apply_along_axis mode for user-supplied
          # vector function
          function = mode
  
          # Create a new padded array
          rank = list(range(narray.ndim))
          total_dim_increase = [np.sum(pad_width[i]) for i in rank]
          offset_slices = tuple(
              slice(pad_width[i][0], pad_width[i][0] + narray.shape[i])
              for i in rank
          )
          new_shape = np.array(narray.shape) + total_dim_increase
          newmat = np.zeros(new_shape, narray.dtype)
  
          # Insert the original array into the padded array
          newmat[offset_slices] = narray
  
          # This is the core of pad ...
          for iaxis in rank:
              np.apply_along_axis(
                  function,
                  iaxis,
                  newmat,
                  pad_width[iaxis],
                  iaxis,
                  kwargs,
              )
          return newmat
  
      # If we get here, use new padding method
      newmat = narray.copy()
  
      # API preserved, but completely new algorithm which pads by building the
      # entire block to pad before/after `arr` with in one step, for each axis.
      if mode == "constant":
          for axis, (
              (pad_before, pad_after),
              (before_val, after_val),
          ) in enumerate(zip(pad_width, kwargs["constant_values"])):
              newmat = _prepend_const(
                  newmat, pad_before, before_val, axis
              )
              newmat = _append_const(newmat, pad_after, after_val, axis)
  
      elif mode == "edge":
          for axis, (pad_before, pad_after) in enumerate(pad_width):
              newmat = _prepend_edge(newmat, pad_before, axis)
              newmat = _append_edge(newmat, pad_after, axis)
  
      elif mode == "linear_ramp":
          for axis, (
              (pad_before, pad_after),
              (before_val, after_val),
          ) in enumerate(zip(pad_width, kwargs["end_values"])):
              newmat = _prepend_ramp(
                  newmat, pad_before, before_val, axis
              )
              newmat = _append_ramp(newmat, pad_after, after_val, axis)
  
      elif mode == "maximum":
          for axis, (
              (pad_before, pad_after),
              (chunk_before, chunk_after),
          ) in enumerate(zip(pad_width, kwargs["stat_length"])):
              newmat = _prepend_max(
                  newmat, pad_before, chunk_before, axis
              )
              newmat = _append_max(newmat, pad_after, chunk_after, axis)
  
      elif mode == "mean":
          for axis, (
              (pad_before, pad_after),
              (chunk_before, chunk_after),
          ) in enumerate(zip(pad_width, kwargs["stat_length"])):
              newmat = _prepend_mean(
                  newmat, pad_before, chunk_before, axis
              )
              newmat = _append_mean(
                  newmat, pad_after, chunk_after, axis
              )
  
      elif mode == "median":
          for axis, (
              (pad_before, pad_after),
              (chunk_before, chunk_after),
          ) in enumerate(zip(pad_width, kwargs["stat_length"])):
              newmat = _prepend_med(
                  newmat, pad_before, chunk_before, axis
              )
              newmat = _append_med(newmat, pad_after, chunk_after, axis)
  
      elif mode == "minimum":
          for axis, (
              (pad_before, pad_after),
              (chunk_before, chunk_after),
          ) in enumerate(zip(pad_width, kwargs["stat_length"])):
              newmat = _prepend_min(
                  newmat, pad_before, chunk_before, axis
              )
              newmat = _append_min(newmat, pad_after, chunk_after, axis)
  
      elif mode == "reflect":
          for axis, (pad_before, pad_after) in enumerate(pad_width):
              if narray.shape[axis] == 0:
                  # Axes with non-zero padding cannot be empty.
                  if pad_before > 0 or pad_after > 0:
                      raise ValueError(
                          "There aren't any elements to reflect"
                          " in axis {} of `array`".format(axis)
                      )
                  # Skip zero padding on empty axes.
                  continue
  
              # Recursive padding along any axis where `pad_amt` is too large
              # for indexing tricks. We can only safely pad the original axis
              # length, to keep the period of the reflections consistent.
              if ((pad_before > 0) or (pad_after > 0)) and newmat.shape[
                  axis
              ] == 1:
                  # Extending singleton dimension for 'reflect' is legacy
                  # behavior; it really should raise an error.
                  newmat = _prepend_edge(newmat, pad_before, axis)
                  newmat = _append_edge(newmat, pad_after, axis)
                  continue
  
              method = kwargs["reflect_type"]
              safe_pad = newmat.shape[axis] - 1
              while (pad_before > safe_pad) or (pad_after > safe_pad):
                  pad_iter_b = min(
                      safe_pad, safe_pad * (pad_before // safe_pad)
                  )
                  pad_iter_a = min(
                      safe_pad, safe_pad * (pad_after // safe_pad)
                  )
                  newmat = _pad_ref(
                      newmat, (pad_iter_b, pad_iter_a), method, axis
                  )
                  pad_before -= pad_iter_b
                  pad_after -= pad_iter_a
                  safe_pad += pad_iter_b + pad_iter_a
              newmat = _pad_ref(
                  newmat, (pad_before, pad_after), method, axis
              )
  
      elif mode == "symmetric":
          for axis, (pad_before, pad_after) in enumerate(pad_width):
              # Recursive padding along any axis where `pad_amt` is too large
              # for indexing tricks. We can only safely pad the original axis
              # length, to keep the period of the reflections consistent.
              method = kwargs["reflect_type"]
              safe_pad = newmat.shape[axis]
              while (pad_before > safe_pad) or (pad_after > safe_pad):
                  pad_iter_b = min(
                      safe_pad, safe_pad * (pad_before // safe_pad)
                  )
                  pad_iter_a = min(
                      safe_pad, safe_pad * (pad_after // safe_pad)
                  )
                  newmat = _pad_sym(
                      newmat, (pad_iter_b, pad_iter_a), method, axis
                  )
                  pad_before -= pad_iter_b
                  pad_after -= pad_iter_a
                  safe_pad += pad_iter_b + pad_iter_a
              newmat = _pad_sym(
                  newmat, (pad_before, pad_after), method, axis
              )
  
      elif mode == "wrap":
          for axis, (pad_before, pad_after) in enumerate(pad_width):
              # Recursive padding along any axis where `pad_amt` is too large
              # for indexing tricks. We can only safely pad the original axis
              # length, to keep the period of the reflections consistent.
              safe_pad = newmat.shape[axis]
              while (pad_before > safe_pad) or (pad_after > safe_pad):
                  pad_iter_b = min(
                      safe_pad, safe_pad * (pad_before // safe_pad)
                  )
                  pad_iter_a = min(
                      safe_pad, safe_pad * (pad_after // safe_pad)
                  )
                  newmat = _pad_wrap(
                      newmat, (pad_iter_b, pad_iter_a), axis
                  )
  
                  pad_before -= pad_iter_b
                  pad_after -= pad_iter_a
                  safe_pad += pad_iter_b + pad_iter_a
              newmat = _pad_wrap(newmat, (pad_before, pad_after), axis)
  
      return newmat
  
imports: from __future__ import division, absolute_import, print_function
  import numpy
  from numpy.core.overrides import array_function_dispatch
label_desc: Use image node_id 27 for calling a global function with example usage: pad(array, pad_width, mode) and returns: newmat, newmat